Action Research for Graduate Program Improvements: A Response to Curriculum Mapping and Review
Bibliographic record
Abstract
There is a global trend toward improving programs and student experiences in higher education through curriculum review and mapping of degree programs. This paper describes an action research approach to program improvement for a course-based MEd degree. The driver for continual program improvement came from actions and recommendations that arose from an institutionally mandated, year-long, faculty led curriculum review of professional graduate programs in education. Study findings reveal instructors’ perceptions about how they enacted the recommendations for program improvement, including (1) developing a visual conceptualization of the program; (2) improved connections between the courses; (3) articulation of coherence in goals and expectations for students and instructors; (4) an increased focus on action research; (5) increased ethics support and scaffolding for students; and (6) the fostering of communities of practice. Study findings highlight strengths of the current program and course designs, action items, and research needed for continual program improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".